Local AI Generation MCP Server (local_ai_gen)

This project runs a local MCP (Model Context Protocol) server that exposes tools for:

1. Text to Image
2. Text to Music / Audio
3. Text to Speech
4. Image/Text to 3D Model

Models Used

• Image Generation: segmind/SSD-1B A fast, distilled version of SDXL that works well on consumer GPUs.
• Music Generation: stabilityai/stable-audio-open-1.0 A high-quality open model for generating sound effects, short music tracks, and ambient audio. (Note: Requires accepting license terms)
• Speech Generation: Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice A robust, multilingual text-to-speech model.
• 3D Generation: stabilityai/TripoSR A fast feed-forward model for image-to-3D reconstruction.

Requirements

• Python 3.10+
• NVIDIA GPU (8GB+ VRAM recommended for running everything smoothly)
• Hugging Face User Access Token (for stable-audio-open-1.0)

Complete Setup Guide

1. Preparation and Hugging Face Authenication

The Stable Audio Open model is gated and requires you to accept its license before downloading.

1. Go to stabilityai/stable-audio-open-1.0 on Hugging Face.
2. Log in and click to Accept the Terms/License.
3. Generate a User Access Token in your Hugging Face Settings.
4. Run the Hugging Face CLI login locally:

   pip install -U "huggingface_hub"
   hf auth
   

   (Paste your token when prompted. You do not need to add it as a git credential).

2. Environment Installation

python -m venv .venv
source .venv/bin/activate
python setup.py

What python setup.py does:

1. Creates output directories (generated_images, generated_audio, generated_models, models)
2. Clones third_party/TripoSR if it is missing
3. Installs everything from requirements.txt
4. Compiles and installs torchmcubes against your active torch version (needed for 3D generation)
5. Installs flash-attn.

Run the MCP server

To run the MCP server manually (via standard I/O):

python mcp_server/main.py

Note: The first run of any specific tool will be slower because it has to download the weights for that model into your Hugging Face cache.

Smoke Test

You can run the included smoke test script to verify all models are working correctly:

python smoke_test_generate.py

MCP Tools Exposed

• generate_image
• generate_audio
• generate_speech
• generate_3d_model
• health_check

Notes

• Generated files are written to generated_images, generated_audio, and generated_models by default.
• For generate_audio and generate_speech, you can override the destination with:
• tool arg output_dir (highest priority), or
• env var GENAI_OUTPUT_AUDIO_DIR
• For generate_image, override destination with tool arg output_dir or env var GENAI_OUTPUT_IMAGE_DIR
• For generate_3d_model, override destination with tool arg output_dir or env var GENAI_OUTPUT_MODEL_DIR

Using with MCP Clients (Cursor, Claude Desktop, etc.)

To use this server in an MCP-compatible client, add the following to your mcp.json (or the respective MCP configuration file for your client). Make sure to replace <YOUR_PROJECT_PATH> with the absolute path to where you cloned this repository:

{
  "mcpServers": {
    "local_ai_gen": {
      "command": "<YOUR_PROJECT_PATH>/.venv/bin/python",
      "args": [
        "<YOUR_PROJECT_PATH>/mcp_server/main.py"
      ],
      "env": {}
    }
  }
}